Data-driven marketing: How to monitor and maintain your data quality

“The price of light is less than the cost of darkness,” said renowned market researcher, Arthur C. Nielsen, in explaining the importance of businesses investing in the quality of their data.

According to IDG, poor data quality is one of the modern-day marketer’s top three concerns. The tools available to marketing professionals for data analysis are evolving rapidly, but despite more and more user-friendly solutions entering the market, the issues of data accuracy remain constant. 

IDG’s report highlights that accuracy remains the “top priority” among 66% of marketing professionals, yet a recent survey by Demand Gen Report revealed that more than 62% of organisations still rely on prospect data that is 20-40% incomplete or inaccurate. Additionally, almost 85% of businesses said they are operating CRM and/or sales force automation databases with between 10-40% bad records.

The cost of poor data can be catastrophic for marketers, with consequences ranging from the distortion of campaign success metrics, to a failure of marketing automation initiatives and loss of revenue. According to Gartner, “Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on data warehouse and customer relationship management (CRM)” – a modern-day qualification of Arthur C. Nielsen’s quote about light and dark.  

With so much at stake, it is therefore unsurprising that the “Data-Driven Marketing Research Summary Report” from Ascend2 highlights that increased revenue attributable to marketing is a top data-driven objective for 44% of organisations in 2015, followed by more sales-qualified leads (42%) and more accurate campaign targeting (41%).

“Accurate data is the foundation of any good marketing strategy, as it offers valuable insights into your business and opportunities for future direction,” says Juanita McGowen, marketing analyst for Parker Hannifin.

“Quality data can tell you what stage someone is at in their customer journey, prompt likely engagement triggers and highlight the channels that people will be most receptive to. That’s a good starting point for effective marketing, as you’re more likely to focus your spending on communications with the right people.”

Data quality = success?

Quality is often used to describe the degree to which data is accurate, complete, and timely. Yet, for marketers, this can often mean delving into a legacy of data evasion. 

Unlike other areas of business such as finance or operations, which are more likely to have data quality established in their processes, marketing has historically  been more focused on messages/content, media buying, creative and propositions, and often data quality was not seen as a priority. It’s only in recent years that marketing has realised the critical nature of using good data.

“If marketing does not focus on data quality, the output and insight from data-driven marketing will be inaccurate and may lead to the wrong decisions being made or actions taken,” says Marie Myles, member of the IDM Data Council and director of consulting, consumer insights and targeting at Experian Data Quality.

“Marketers tend to think that data quality is the remit of the IT department. Whilst IT plays an important role in data quality management, marketing needs to work with IT to ensure marketing data is captured and managed correctly. Leaving it to IT alone may lead to the wrong data being quality managed.”

Indeed, data success is often underpinned by how well IT and marketing departments tackle the issue of data quality together. In the research report, ‘Dawn of the CDO’, Experian found that 90% of organisations say data is changing the way they do business, and that as a result, CIOs are facing major challenges with volumes of data (44%) and real-time processing (44%).

Gartner research highlights the only viable solution is to create more synergy between marketing and IT cohorts in order to tackle the issue of data quality, and predicts that businesses currently building better relationships between the two departments could potentially experience an ROI improvement of up to 25% by 2018, compared with those that aren’t.

Establishing the quality of data

For those organisations that are serious about data-driven marketing, establishing data quality requires a series of vital actions.

"One of the things that has come up a lot recently with the new Information Commissioner's Office (ICO) guidance is doing the correct due diligence into your data providers and the data they supply you," says John Mitchison, head of preference services, compliance and legal at The DMA. "It is no longer enough to just ask for data and seek a couple of reassurances from the supplier about where it came from and whether it was properly permissioned. You actually have to see some evidence yourself." 

“When it comes to buying data, obviously telephone data is quite high profile at the moment - there is a lot of stuff about telephone preference service (TPS) complaints. If you’re buying data you should have all the standard contracts and NDAs in place and you should be familiar with the supplier you were buying from, but you would want to know where the data was collected. If it was collected over the phone you want to see a script, you might even want to hear call recordings. If it was collected online you want to see the actual website, if the site is still there, or copies of the site, and then even when it comes to using the data I wouldn’t use it all in one batch – run a  couple of tests with the data.” 

“The starting point should be to establish where the data came from,” McGowen adds. “Is it primary research, paid-for secondary data or inherited information?

“Primary data has the advantage that you should know how, when and why it was collected, and can therefore make judgements about its likely validity. If you’re responsible for data protection, the ‘why’ is very important – as UK law requires firms to be clear and transparent about how they use information held about individuals. For example, if you collect information from a customer about the servicing and repair of a product, that does not give you the automatic right to send them promotional emails about the launch of other new company products; you need to be clear of the purpose for any data collection. So getting to grips with the parameters already established and working out any gaps that need filling will be high on the agenda.

“Secondary data (purchased or inherited information) can also be very useful to business, as long as you know the information’s pedigree. Many marketers buy data from specialist collectors, such as media contacts or lifestyle survey companies. If that includes you, always check how the data provider builds and maintains its information – do people have to register or ‘opt-in’ to be included? It’s also important to understand how frequently information is updated and what rights you have to use it. Some companies sell data for multiple uses, whilst others may grant a single-use licence.”

McGowen suggests involving other departments to ask the following, initial questions around the quality of your marketing data:

• How reliable do your colleagues think the data is, and why?
• Has the data been reviewed and maintained since it was collected?
• How much (and what kind of) data do you really need to carry out marketing activity?
• Has an audit been run recently to check for duplicate information and consistency?

Data decay is a guarantee for all marketing databases – contacts’ email addresses change as they move from one company to another, unqualified leads opt out of communications. While auditing can be a painfully long process, it is vital that a process is put in place to ensure data is not allowed to degrade.

Data governance structures

Businesses should also have the correct structures and processes in place to ensure that the basic levels of governance and quality are met. 

“It isn’t enough to simply collect data,” notes former D&B global chief data, insight and analytic officer Paul Ballew.

“You must also establish a data governance structure that brings together all of your data and third-party assets in a systematic way to ensure data quality. This is critical as you look to build your marketing organisation's data strategy in the year ahead.”

Ballew believes that best practice for data governance should include:

• Defining your data standards, including the metrics for adhering to those standards.
• Ensuring data quality at the point of origin and at key checkpoints as data flows through your organisation’s systems and databases.
• Adopting a unique, persistent key that identifies each entity, such as a customer, and the corresponding data that relates to that entity.
• Establishing a nomenclature and taxonomy to identify, categorise and organise your data.
• Implementing a rigorous data maintenance strategy to update constantly changing information.

“Each of these practices must be applied enterprise-wide to ensure consistent handling of data across the entire organisation,” he explains. “And if you are feeling overwhelmed by the demands of rigorous data governance, don’t worry, most companies are—or have been—in your shoes. Few companies have all of the necessary capabilities or the global datasets to address all their business objectives.”

And to even come close to meeting business objectives through data means establishing a code for training and coaching data collectors, which allows marketers to get to grips with how dataflow processes work, and understand how to identify and eliminate potential risks and spot opportunities.

Juanita McGowen adds that consistent data management is important, especially where collecting data across multiple systems: “Otherwise you could collect information in different formats or using different field types, which will reduce the potential to integrate customer insights. Linked to this is the need to have a permissions-based structure, so that employees only see what’s appropriate for their roles.

“Checking the transparency of your data collection methods used can highlight potentially weak processes or scenarios that impact upon customer perceptions. Ambiguous sign-up forms or automatic opt-ins can work against some businesses, if customers feel coerced into providing their details.

“And of course, allocating processes and resources to maintain and update information systems will allow you to prolong the life and usefulness of your data.”

Whilst data governance is not a hardware, software, or manpower solution, it does rely on the use of appropriate software and technology which is integrated into the appropriate systems/processes with a business, including validation tools at the point of data capture, eg address, email address and data quality checking tools.

Best practices

Best practice for ensuring data quality often boils down to establishing detailed and regular processes across functions, which can be complex. However, Marie Myles states that the following checklist can help marketers through the process:   

• Take data seriously - have it as part of the business strategy.
• Think and plan ahead – what are you trying to achieve (business goals) and then identify which data is required to do this and focus data quality in these areas.
• A data champion is critical for success – Chief data officers (CDOs) are becoming more prevalent as data is an asset.
• Senior sponsors are required – ensure investment and resource is allocated to the tasks.
• Clear rules and procedures – these need to be shared and understood by all parts of the business and are essential; don’t allow different departments to go their own way as this will undermine your ability to get a ‘single version of the truth’.
• Ensure all staff know why data quality is important – and what role they play in it, eg call centre staff collecting and updating records, marketing managing codes and metadata.
• Be ruthless about the volume and type of data managed – particularly latency. Do you really need to store data for long periods of time?

“Invest the time and resources to build an effective governance structure,” adds Paul Ballew. “Without strong foundational data assets, you won’t be able to handle your terabytes of structured and unstructured data or leverage analytics for predictive insights. Big Data isn’t just about the data, but about taking all of the right steps to organise your data and third-party assets in a systematic way to ingest, curate and make sense of the data. It is a transformational path – but one that will lead to the Holy Grail of marketing: growth.”